lukoshkin

FROM ubuntu:20.04

ARG UID=1000
ARG GID=1000

ENV USER rust 
ENV HOME /home/$USER
ENV PATH "$HOME/.cargo/bin:$PATH"

WORKDIR /tmp

lukoshkin

OBJDIR = bin
DEPDIR = deps
DEPFLAGS = -MT $@ -MMD -MP -MF $(DEPDIR)/$*.d

src = $(wildcard src/*.cc)
obj = $(src:src/%.cc=$(OBJDIR)/%.o)
DEPFILES = $(src:src/%.cc=$(DEPDIR)/%.d)


# if boost is correctly installed, no need to add pathes to

lukoshkin

FROM ubuntu:18.04

ENV USER mpitest
ENV HOME /home/$USER
ENV MPI_DIR=/opt/ompi
ENV PATH="$MPI_DIR/bin:$HOME/.local/bin:$PATH"
ENV LD_LIBRARY_PATH="$MPI_DIR/lib:$LD_LIBRARY_PATH"
WORKDIR $HOME

RUN apt-get -q update \

lukoshkin

# Task: convert persistence landscape given in the form (N, k, n, 2)
# to its another representation of the shape (N, k, n, p), where N is
# the batch size, k - number of feature dimensionalities, n - number of
# layers. And the last dimension is 2 dates of event's birth and death
# in the former brackets and p is number of discretization steps
# in the latter, on the interval where these event dates are defined.
import torch
import matplotlib.pyplot as plt

def triangle_hat(x, a, b):

lukoshkin

import numpy as np
import scipy.sparse as scsp

def GKL_bidiagonalization(A):
    A = np.matrix(A)
    m,n = A.shape
    
    p = max(m, n)
    alpha = np.zeros(p)
    beta = np.zeros(p-1)

lukoshkin

import numpy as np


def pwCDF(fn, m, interval=(0, 1)):
    """
    fn          probability density function
    m           number of intervals at which `fn` is approximated
    interval    `fn`'s domain
    """
    A, B = interval

lukoshkin

def calc_grad_penalty(real_samples, fake_samples, net_D):
    """
    Evaluates gradient penalty for `net_D` and allows other gradients
    to backpropogate through this penalty term
    Args:

        real_samples - a tensor (presumably, without `grad` attribute)
        fake_samples - tensor of the same shape as `real_samples` tensor
        net_D - a 'critic' which takes the input of the same shape
                as `real_samples`

lukoshkin

import cv2
import numpy as np

def ACD(filename):
    """
    Calculates Average Content Distance for the given short video
    
    Parameters:
    -----------
        filename : str or path-like - path to .mp4 or .gif file